Update README.md

README.md

@@ -24,18 +24,16 @@ The training recipe was based on wsj recipe in [espnet](https://github.com/espne
 
 This model is Hybrid CTC/Attention model with pre-trained HuBERT as the encoder.
 
-This model trained on Thai-central for being the supervised pre-trained model in transfer-based curriculum learning experiment.
+This model was trained on Thai-central to be used as a supervised pre-trained model in order to be used for finetuning to other Thai dialects. (Experiment 2 in the paper).
 
-you can demo on colab with [this link](https://colab.research.google.com/drive/1stltGdpG9OV-sCl9QgkvEXZV7fGB2Ixe?usp=sharing). (Please note that you cannot inference >4 seconds of audio with free Google colab)
+We provide some demo code to do inference with this model on colab [here](https://colab.research.google.com/drive/1stltGdpG9OV-sCl9QgkvEXZV7fGB2Ixe?usp=sharing). (Please note that you cannot inference >4 seconds of audio with free Google colab)
 
 
 ## Evaluation
 
 <!-- This section describes the evaluation protocols and provides the results. -->
 
-For evaluation, the metrics are CER and WER. before WER evaluation, transcriptions were re-tokenized using newmm tokenizer in [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp)
-
-In this reposirity, we also provide the vocabulary for building the newmm tokenizer using this script:
+For evaluation, the metrics are CER and WER. Before WER evaluation, transcriptions were re-tokenized using newmm tokenizer in [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp)
 
 ```python
 from pythainlp import word_tokenize